Optimizing Production Process through Production Planning and Inventory Management in Motorcycle Chains Manufacturer

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1 ComTech: Computer, Mathematics and Engineering Applications, 9(2), December 2018, DOI: /comtech.v9i P-ISSN: E-ISSN: X Optimizing Production Process through Production Planning and Inventory Management in Motorcycle Chains Manufacturer Shelvy Kurniawan 1 and Steven Sanjaya Raphaeli 2 1,2 Management Department, BINUS Business School Undergraduate Program, Bina Nusantara University Jln. K. H. Syahdan No. 9, Jakarta Barat 11480, Indonesia 1 shelvy.kurniawan001@binus.ac.id; 2 stevensanraphaeli@gmail.com Received: 30 th April 2018/ Revised: 30 th May 2018/ Accepted: 5 th June 2018 How to Cite: Kurniawan, S., & Raphaeli, S. S. (2018). Optimizing Production Process through Production Planning and Inventory Management in Motorcycle Chains Manufacturer. ComTech: Computer, Mathematics and Engineering Applications, 9(2), Abstract - Based on the data, there were still shortages of production from year to year and demand were unstable in motorcycle chains manufacturer in Indonesia. To overcome these problems, the purpose of this research was to make production planning and inventory control consisting of forecasting, aggregate planning, Master Production Schedule (MPS), and Material Requirements Planning (MRP). Forecasting used the additive decomposition method (average of all data), multiplicative decomposition (centered on moving average), and winter method (additive and multiplicative). Aggregate planning used chase strategy, level strategy, and transportation model. Moreover, MRP used lot for lot, Economic Order Quantity (EOQ), and Periodic Order Quantity (POQ) methods. The test shows several results. First, the best forecasting is additive decomposition (average of all data) with MAD value of 3.033,57, MSE with , and MAPE with 10,083%. Second, the best aggregate planning is transportation model with the total cost of Rp ,00. Last, the best MRP method is the lot for lot with total cost Rp ,00. Keywords: production process, production planning, inventory management I. INTRODUCTION In motorcycle chains manufacturer, there are two main types of motorcycle chains produced. Those are drive chain and cam chain. Drive chain is used on motorcycle wheels, while cam chain is on engine parts. Drive chain consists of 428-H, 428, 420, and 520 types. Meanwhile, the cam chain is 25, 25-H, and 25-SH types. Table 1 summarizes the demand and production data for each motorcycle chain. In Table 1, it can be analyzed that the company has a problem in forecasting the demand. It is seen from the error rate in every type of chains. Moreover, the biggest error rate comes from 428-H drive chain. The lack of production is due to the poor control that mainly lies in production planning and inventory management. Therefore, it is difficult for the quantity of production to meet the demand from customers. Table 1 Demand and Production in October September 2017 Type Demand Production Production vs demand Error rate (%) 25-SH ,90 25-H , , H , , , ,42 Production planning and inventory control studied in this research are for 428-H type of drive chain. It is because 428-H type has the biggest production deficiency level in the last three years. It is about 16,51% as presented in Table 1. Lack of production reduces the profits that should be earned by the company directly. Thus, the company loses the opportunity to optimize their profit. The lost sales and revenue of the company for 428-H drive chain type can be seen in Table 2 and Table 3. Besides considering the biggest forecast error in 428-H drive chain, this research also focuses on 428-H because this type gives the highest contribution to the company revenue about 30,06% as presented in Figure 1. Copyright

2 Table 2 Demand and Production of 428-H Type Year Demand Production Gap Total Table 3 Lost Revenue of 428-H Type Lost Sales(Unit) Price/Unit(Rp) Lost Revenue(Rp) , ,00 Figure 1 Revenue Contribution in October September 2017 Based on the previous explanation, this research focuses on production planning and inventory management. It will be started with one of the crucial parts in production planning and inventory control, that is the forecasting process. This is in line with the statement from Chen and Blue in Rakićević and Vujošević (2015). They said that all issues of a supply chain planning system started with demand forecasting. It served as the basis of every planning activity. Vujosevic in Rakićević and Vujošević (2015) also explained forecasting as the art and science of predicting future events. Moreover, Reid and Sanders (2011) explained that there were four types of demand patterns in forecasting methods. First, it was level or horizontal pattern. This pattern had no trend and stationary. Second,there was a trend pattern. This pattern has a stable tendency to grow or decrease. Third, the seasonal pattern had repeated fluctuations of data at any given interval. Fourth, it was a cyclical pattern. This pattern was similar to a seasonal pattern. For example, the length and magnitude of the cycle might vary due to business and economic fluctuations. Production planning and inventory control are not only the forecasting stage, but it also progresses to the aggregate planning process. The importance of aggregate planning to match capacity with demand is explained by Chen and Huang (2010). They stated that in highly competitive and fluctuating business environment, companies around the world had increasingly emphasized on aggregate production planning. It was for determining the best way to match capacity with demand forecasting. It was also for varying customers orders over the medium term by adjusting regular and overtime production rates, subcontracting and back ordering rates, inventory levels, labor levels, and other controllable factors. Aggregate planning is an approach to determine the quantity and time of production in the intermediate term. It is usually in 3 to 18 months (Heizer, Render, & Munson, 2017). According to Stevenson (2015), there are several strategies in aggregate planning. It consists of level capacity strategy, chase demand strategy, and transportation model. Heizer, Render, and Munson (2017) explained that the level strategy was an aggregate plan in which production was similar from period to period. To maintain the constant output rate, the company could let the finished goods inventory vary to buffer the difference between demand and production. Meanwhile, the chase strategy was a planning strategy that set production to be equal to demand forecasting. For transportation model, Stevenson (2015) explained that aggregate planning would adjust the capacity to the required demand while minimizing cost. Based on the aggregate planning result, MPS and MRP can be planned. According to Heizer, Render, and Munson (2017), MPS plans what and when the production is. Meanwhile, MRP is a dependent demand technique that uses bill-of-materials, inventory, expected receipts. Islam, Rahman, Saha, and Saifuddoha (2013) suggested that MRP as a material control system. It attempted to keep adequate inventory levels to assure that the required materials were available when it was needed. The relationship between MRP and MPS is explained by Gharakhani (2011). MRP is a plan for the production and purchase of the components used in making items in the MPS. Furthermore, Heizer, Render, and Munson (2017) also stated that there were three lot sizing methods. Those can be used in deciding number of orders in MRP. Those are the lot for lot, Economic Order Quantity (EOQ), and Periodic Order Quantity (POQ). Stevenson (2015) explained the differences between lot for lot and EOQ model. First, in the lot for lot model, the order for each period was equal to the demand in that period. Second, in EOQ model, it identified the optimal order quantity by minimizing the total of the special annual cost. It varied from the size and frequency of order. In POQ model, Heizer, Render, and Munson (2017) stated that it was a lot sizing technique by ordering the required quantity for a specified time between orders such as in every three weeks. Based on the previous explanation, it can be seen that all the processes starting from forecasting, aggregate planning, MPS, and MRP are needed to optimize the production process. There are several literature reviews used in this research. Akpinar and Yumusak (2016) discussed the decomposition forecasting and aggregate production planning methods such as chase strategy, level strategy, and hybrid strategy, and MRP using DOQ and EOQ. Several 44 ComTech: Computer, Mathematics and Engineering Applications, Vol. 9 No. 2 December 2018, 43-50

3 methods mentioned are also used in this research. However, the researchers also add the other methods such as winter additive and multiplicative methods in the processing of demand forecasting. It is because there may be a seasonal pattern by using the minitab application to expand the choice of forecasting methods that can be selected. Dinesh, Arun, and Pranav (2014) explained the relationship between forecasting and MRP. Based on the forecasted demand, MRP was done. Then, orders were done to purchase materials, and the materials were stocked. There are four purposes for this research. First, the researchers analyze which forecasting method is the most appropriate to be applied in motorcycle chains manufacturer. It is based on the historical demand pattern and the smallest error rate on the 428-H type. Second, the researchers analyze which aggregate planning methods that produce the smallest cost in fulfilling the company s demand. Third, the researchers create MPS that can meet all demands for 428-H type in October 2017 to September Last, the researchers analyze MRP to search the lowest cost of lot sizing method which can be used by motorcycle chains manufacturer. II. METHODS This research is a descriptive research. The data collection in this research is a cross-sectional. According to Sekaran and Bougie (2013), in cross-sectional research, the data are taken only once in days, weeks, or months to answer research questions. This research begins by retrieving data of orders from the company. Then, the data are used to determine the demand pattern. The demand pattern based on the demand graph is seasonal. Some suitable forecasting methods for the seasonal pattern are selected. They are additive decomposition method (average of all data), additive decomposition (center moving average), multiplicative decomposition (average of all data), multiplicative decomposition (center moving average), winter additive method, and winter multiplicative method. The researchers provide several forecasting method tests to search for the best forecasting method. The method should suit the company s demand pattern. Aras and Gülay (2017) mentioned that to improve the forecasting accuracies, researchers had been using various combination techniques. In particular, the use of dissimilar methods for forecasting time in data was expected to provide superior results. In this research, to choose the most suitable forecasting method for the company, error rate checking with Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE) is done. According to Chopra and Meindl (2010), MAD measures the total error in the forecast regardless of the signs. MSE can be associated with variants of forecast error. Meanwhile, MAPE is the average of the absolute error value described as a percentage of the real value. Then, the method which has the smallest error rate is used to do the aggregate planning. In this research, aggregate planning is done using three methods. Those are chase strategy, level strategy, and transportation model. Then the aggregate planning method is chosen based on the smallest cost. The selected aggregate planning method is used in making MPS and MRP. MRP consists of the lot for lot, EOQ, and POQ method. Then, the best MRP method is selected based on the smallest cost. III. RESULTS AND DISCUSSIONS The results contain the calculation according to the method described. Results and discussion are conducted sequentially starting from forecasting, aggregate planning, MPS, and MRP. The first thing to be discussed is about forecasting. Analysis of demand pattern is done using data from October 2014 until September It is shown in Figure 2. Figure 2 Demand Pattern of 428-H From Figure 2, it can be seen that the demand has a seasonal pattern. This seasonal pattern is present in every May and June. The demands in both periods are always higher than the other periods. Based on the interview with the company, demand in May and June is high because the months approach Eid Al-Fitr day (Eid Al-Fitr in 2015 was on July 17, Eid Al-Fitr in 2016 was on July 6, and Eid Al- Fitr in 2017 was on 25 June). Thus, many manufacturers prepare the stock of goods for sale in that peak season. From Figure 1, it can also be seen that demand after the peak season (Eid Al-Fitr period) always goes down and it happens every year. By knowing the data pattern of the company s demand, the appropriate method to forecast the company s demand can be determined. Some forecasting methods that match the seasonal data pattern and are used in this research are: (1) additive decomposition (centered moving average), (2) additive decomposition (average of all data), (3) multiplicative decomposition (centered on moving average), (4) multiplicative decomposition (average of all data), (5) winter (additive method), and (6) winter (multiplicative method). In Table 4, it shows the error rate for each forecasting method. The seasonal additive decomposition (average of all data) method is the method with the smallest MAD and MSE levels. MAD equals to 3.033,571 and MSE is Regarding MAPE, the smallest error comes from multiplicative decomposition method (average of all data). Although the additive decomposition method does not have the smallest error in MAPE, the MAPE score is close to the smallest score. MAPE score for multiplicative decomposition (average of all data) is equal to 10,074. Meanwhile, MAPE for additive decomposition (average of all data) is 10,083. Thus, it can be concluded that the additive decomposition (average of all data) is the most accurate forecasting method. Therefore, this method is used to find the forecast value of the next period. The forecasting result is used in the aggregate planning. The additive Optimizing Production Process... (Shelvy Kurniawan and Steven Sanjaya Raphaeli) 45

4 decomposition (average of all data) method is shown in Table 5. Table 4 Error Level Comparison of Several Forecasting Methods Forecasting Method MAD MSE MAPE(%) Additive Decomposition (Centered Moving Average) Additive Decomposition (Average of All Data) Multiplicative Decomposition (Centered Moving Average) Multiplicative Decomposition (Average of All Data) Winter (Additive Method) Winter (Multiplicative Method) ,690,960 11, ,590,490 10, ,540,650 11, ,658,400 10, ,918, Month Table 6 Production Capacity Working Days Regular Capacity (1380/day) Overtime Capacity Subcontract Capacity Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Total Table 5 Forecasting Result using Additive Decomposition (Average of All Data) Year Month Forecast Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep After getting the forecasting result, the researchers do the aggregate planning. To make the aggregate plan, the required data are forecasted demand, production capacity data, and cost data. Table 6 contains production capacity data, both regular, overtime, and subcontracts that have been adjusted to the working day during the forecasting period. The regular monthly capacity is a regular production capacity in a day multiplied by the number of working days in a month. Then, overtime capacity is based on daily overtime capacity multiplied by the number of working days per month. In Table 7, the list of the costs used in aggregate planning processing is presented. Table 7 Cost in Aggregate Planning Type of Cost Regular time cost Overtime Cost Subcontract Cost Holding Cost Backorder Cost Lost Sales Cost Amount Rp19.650,00/ unit Rp20.028,00/ unit Rp23.000,00/ unit Rp282,00/ unit Rp3.925,00/ unit Rp28.988,00/ unit Then, all the data is processed using POM QM software. It calculates the cost of each method of aggregate planning. The result is presented in Table 8. Cost Table 8 Cost Comparison of Several Aggregate Planning Methods Chase Strategy (Rp) Level Strategy (Rp) Transportation Model (Rp) Regular Time , , ,00 Overtime , , ,00 Subcontracting , ,00 0 Inventory , ,00 Shortage , ,00 0 Total , , ,00 46 ComTech: Computer, Mathematics and Engineering Applications, Vol. 9 No. 2 December 2018, 43-50

5 Based on the comparison of costs in Table 8, aggregate planning using transportation model requires the smallest cost compared to other methods. It amounts about Rp This is because the transportation model always puts production which the minimum cost will be incurred. Therefore, aggregate planning with transportation model will be used in planning MPS. Then, MPS is presented in Table 9. Table 9 also shows Projected on Hand (PAB) and Available to Promise (ATP). PAB is the expected amount of inventory at the beginning of each period. Then, ATP is the information about how many available products in specific periods are for the customer to order. Based on the MPS in Table 9, the results obtained in October 2017 shows that the company needs to produce units in the first week, units in the second week, and units in the fourth week. According to the aggregate planning result, the researchers can calculate MRP. In calculating MRP, the primary data needed is MPS to determine gross requirements material. In addition, other data such as the bill of material and on hand material are also needed. Table 10 presents materials which are needed to create a 428 H type drive chain. Table 10 presents used materials to make a unit of 428-H drive chain. The materials consist of ILP, OLP, pin, bush, and roller. Inner Link Plate (ILP) is an inner plate of the chain structure. Outer Link Plate (OLP) is on the outside. Pin serves as a load holder that works on the chain. Bush is a bearing on the roller. Then, roller smoothes the movement on the sprocket, because the roller is part of the chain that has direct contact on the sprocket. The requirements are ILP (0,331kg), OLP (0,292kg), pins (0,285 kg), bush (0,153kg), and roller (0,183kg). Next, material on hand is the remaining material from the previous period at the beginning of the next period. Table 11 presents the number of available materials at the beginning of the period of October Based on the data, MRP is analyzed using three methods. Those are the lot for lot, EOQ, and POQ. Then, based on those MRP methods, the total cost is compared. The company selects MRP method with the smallest cost. The comparison of the total cost for each MRP method is presented in Table 12. Based on the information in Table 12, the cost of MRP with lot for lot technique has a smaller cost than EOQ and POQ. This is because the lot for lot method has a minimal cost. The order is done only as much as the quantity needed. This prevents the buildup of inventory in the lot for lot method. In Table 13 to Table 17, MRP calculation using the lot for lot method is presented. In Table 13, on-hand stock of ILP is units. Moreover, the gross requirement for the first week is Thus, numbers of order that should be received in the first week are units. Considering the lead time, the units should be ordered 1 week before the purchase order schedule. Table 9 Master Production Schedule for October and November 2017 On Hand 0 Lot Size Lot Size C.A C.A Month October November Week Forecast Order PAB MPS ATP CUM ATP Demand Material Table 10 Bill of Material 428 H Drive Chain Requirements/ product (unit) Mass/unit (gr) Mass/product (kg) ILP 126 2,627 0,331 OLP 125 2,334 0,292 Pin 124 2,3 0,285 Bush 126 1,212 0,153 Roller 126 1,453 0,183 Optimizing Production Process... (Shelvy Kurniawan and Steven Sanjaya Raphaeli) 47

6 In Table 14, the on-hand stock of OLP is units and the gross requirement is in the first week of October. The numbers of order should be received in the first week are 371 units. Considering the time, 371 units should be ordered one week before the purchase. In Table 15, the on-hand stock of pin is units. Then, the gross requirement for the first week is Since the number of on-hand stock is higher than the number of units needed, the company does not need to open new purchase to the supplier. Similarly, Table 16 shows that the on-hand stock of bush is units. The gross requirement for the first week is The on-hand stock is higher than the number of units needed. Thus, the company does not need to open new purchase to the supplier. In Table 17, the on-hand stock of roller is units, and the gross requirement for the first week is The numbers of on-hand stock are greater than the units needed. Therefore, the company does not need to open new purchase to the supplier. Table 11 Material on Hand at the Beginning of October 2017 Material On Hand (kg) ILP OLP Pin Bush Roller Table 12 Cost Comparison of MRP Method Lot Sizing Method Total of Material Cost (Rp) Lot For Lot ,00 EOQ ,00 POQ ,00 Table 13 MRP using Lot for Lot Method for ILP Material in October to November 2017 Part Number - Description ILP BOM UoM Kg On-hand Gross Requirements PAB Net Requirements Purchase Receipts Purchase Releases 1, Table 14 MRP using Lot for Lot Method for OLP Material in October to November 2017 Part Number - Description OLP BOM UoM Kg On-hand Gross Requirements PAB Net Requirements Purchase Receipts Purchase Releases ComTech: Computer, Mathematics and Engineering Applications, Vol. 9 No. 2 December 2018, 43-50

7 Table 15 MRP using Lot for Lot Method for Pin Material in October to November 2017 Part Number - Description OLP BOM UoM Kg On-hand Gross Requirements PAB Net Requirements Purchase Receipts Purchase Releases Table 16 MRP using Lot for Lot Method for Bush Material in October to November 2017 Part Number - Description Bush BOM UoM Kg On-hand Gross Requirements PAB Net Requirements Purchase Receipts Purchase Releases Table 17 MRP using Lot for Lot Method for Roller Material in October to November 2017 Part Number - Description Roller BOM UoM Kg On-hand Gross Requirements PAB Net Requirements PO Receipts PO Releases Optimizing Production Process... (Shelvy Kurniawan and Steven Sanjaya Raphaeli) 49

8 IV. CONCLUSIONS Based on the research that has been done, there are several conclusions. First, the company should apply the method of forecasting using additive decomposition (average of all data). It can overcome the problem of production shortages as experienced by the company in the last three years. The additive decomposition (average of all data) method can result in more accurate forecasting. It is because the forecasting method conforms to the seasonal data pattern and has the smallest error rate compared to other forecasting methods. From the forecasting result, it can be continued to the aggregate planning. The calculation of aggregate planning in this research is done by using three methods that are chase strategy, level strategy, and transportation model. Based on the three methods, the total cost is calculated to determine the best method based on the lowest total cost. The chosen method in this research is the transportation model. It has the smallest cost compared to other methods. Transportation model produces the smallest total cost because the allowance for excess capacity in the previous period can be used in the period which the demand is high. The cost generated by backorder charges and subcontract costs can be eliminated by this method. Then, from the result of aggregate planning using transportation model, MPS which schedules the weekly production is made. In the made MPS, PAB is positive. This proves that the made MPS can meet customer s demand. MRP in this research uses three lot sizing methods. Those are the lot for lot, EOQ, and POQ. The selection of the best method is made by choosing the smallest total cost of those three methods. In this research, the smallest total cost is generated by lot for lot method. This is because the lot for lot method has a minimal inventory cost. It is due to the order that is done as much as the quantity needed to prevent the accumulation of inventory. Heizer, J., Render, B., & Munson, C. (2017). Operations management: Sustainability and Supply Chain Management (12 th ed.) United States of America: Pearson Education, Inc. Islam, M. S., Ripon Kumar Saha, M., & Mahbubur Rahman, A. M. (2013). Development of Material Requirements Planning (MRP) software with C language. Global Journal of Computer Science and Technology, 13(3), Rakićević, Z., & Vujošević, M. (2015). Focus forecasting in supply chain: The case study of fast moving consumer goods company in Serbia. Serbian Journal of Management, 10(1), Reid, R. D., & Sanders, N. R. (2011). Operation management (4 th ed.). New York: John Willey & Sons, Inc. Sekaran, U., & Bougie, R. (2013). Research methods for business: A skill-building approach (6 th ed.). Chichester: John Wiley & Sons Ltd. Stevenson, W. J. (2015). Operations management (12 th ed.). New York: McGraw-Hill Education. REFERENCES Akpinar, M., & Yumusak, N. (2016). Time series decomposition of natural gas consumption. International Journal of Advances in Science Engineering and Technology, 4(2), Aras, S., & Gülay, E. (2017). A new consensus between the mean and median combination methods to improve forecasting accuracy. Serbian Journal of Management, 12(2), Chen, S. P., & Huang, W. L. (2010). A membership function approach for aggregate production planning problems in fuzzy environments. International Journal of Production Research, 48(23), Chopra, S., & Meindl, P. (2010). Supply Chain Management: Strategy, planning, and operation (4 th ed.). Upper Saddle River, New Jersey: Pearson Education, Inc. Dinesh, E. D., Arun, A. P., & Pranav, R. (2014). Material requirements planning for automobile service plant. International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), 3(3), Gharakhani, D. (2011). Optimization of material requirement planning by goal programming model. Asian Journal of Management Research, 2(1), ComTech: Computer, Mathematics and Engineering Applications, Vol. 9 No. 2 December 2018, 43-50